In recent years, the world has witnessed explosive growth in the demand for wireless communications and it is predicted that this demand will increase in the future. There are already over 500 million users subscribing to cellular telephone services and the number is continually increasing. Eventually, in the not too distant future the number of cellular subscribers will exceed the number of fixed line telephone installations. Already, in may cases, the revenues from mobile services already exceeds that for fixed line services even though the amount of traffic generated through mobile phones is much less than in fixed networks.
Other related wireless technologies have experienced growth similar to that of cellular. For example, cordless telephony, two way radio trunking systems, paging (one way and two way), messaging, wireless local area networks (WLANs) and wireless local loops (WLLs).
Currently, the majority of users subscribe to digital cellular networks. Almost all new cellular handsets sold to customers are based on digital technology, typically second generation digital technology. Currently, third generation digital networks are being designed and tested which will be able to support data packet networks and much higher data rates. The first generation analog systems comprise the well known protocols AMPS, TACS, etc. The digital systems comprise GSM, TDMA (IS-136) or CDMA (IS-95), etc.
Most communication systems must combat a problem known as Intersymbol Interference (ISI). Ideally, a transmitted symbol should arrive at the receiver undistorted, possibly attenuated greatly and occupying only its time interval. In reality, however, this is rarely the case and the received symbols are subject to ISI. Intersymbol interference occurs when one symbol is distorted so much that it occupies the time intervals of other symbols.
A diagram illustrating a transmitted symbol spread across multiple symbol times due to the effects of multipath propagation and filtering is shown in FIG. 1. The graph depicts the received channel impulse response. It illustrates the output signal strength of the channel when only one symbol was transmitted. The ticks define symbol duration times T. A symbol transmitted between times 4 to 5 is spread over eight symbol times.
The situation is made even worse in GSM communications systems as the GSM transmitter contributes its own ISI due to controlled and deliberate ISI from the transmitter's partial response modulator. The effects of ISI are influenced by the modulation scheme and the signaling techniques used in the radio.
Considering ISI caused by the radio channel, multipath fading is the primary component. The problem stems from the fact that the transmitted signal takes alternate paths in addition to the direct path. In some cases, there is no direct path because it is blocked. Each path is characterized by a different delay and reflection coefficient. The fading phenomenon is due to interference between many signal reflections each having different phases. Since the carrier frequency is typically very high in radio channels, any change in the propagation channel greatly affects the interference pattern. This is typically observed as fast channel variations over time. It may be characterized through Doppler spread measurements. Doppler spread is caused by the relative motion between a receiver and a transmitter. Signals arrive at the receive having different frequencies, amplitudes and phase. ISI can also be generated when a signal is passed through a filter too narrow to accommodate the bandwidth of the signal, e.g., the transmitter pulse shaping filter or receive filter. A narrow filter spreads the modulation pulses over time and the channel itself has filter like effects on the transmitted signal. With the radio channel, however, the characteristics of its filter like action vary with time.
Equalization is a well known technique used to combat intersymbol interference whereby the receiver attempts to compensate for the effects of the channel on the transmitted symbols. An equalizer attempts to determine the transmitted data from the received distorted symbols using an estimate of the channel that caused the distortions. In communications systems where ISI arises due to partial response modulation or a frequency selective channel, a maximum likelihood sequence estimation (MLSE) equalizer is optimal. This is the form of equalizer generally used in GSM systems.
The MLSE technique is a nonlinear equalization technique which is applicable when the radio channel can be modeled as a Finite Impulse Response (FIR) system. Such a FIR system requires knowledge of the channel impulse response tap values. It obtains this information by using a known symbol sequence to estimate the channel impulse response. The known symbol sequences are called training sequences or sounding sequences, which the transmitter sends to the receiver at regular intervals.
There exist other equalization techniques such as Decision Feedback Equalization (DFE) or linear equalization. All these equalization techniques require precise knowledge of the channel.
In GSM, the training sequence is sent in the middle of each burst. As shown in FIG. 2, each fixed length burst 10 consists of 142 symbols preceded by 3 tail symbols 12 and followed by 3 tail symbols 20 and 8.25 guard symbols 22. The 142 symbols include a 58 symbol data portion 14, 26 symbol training sequence 16 and another 58 symbol data portion 18. Since the training sequence is sent in the middle of the burst, it is referred to as a midamble. It is inserted in the middle of the burst in order to minimize the maximum distance to a data bit thus minimizing the time varying effects at the ends of the burst.
The training sequences comprise sequences of symbols generated to yield good autocorrelation properties. The receiver control algorithm uses the training sequence, received in the presence of ISI, to determine the characteristics of the channel that would have generated the symbols actually received. GSM uses eight different training sequences whereby the autocorrelation of each results in a central peak surrounded by zeros. The channel impulse response can be measured by correlating the stored training sequence with the received sequence.
The MLSE equalizer (also called a Viterbi equalizer) uses the Viterbi algorithm along with inputs and an estimate of the channel to extract the data. The equalizer generates a model of the radio transmission channel and uses this model in determining the most likely sequence. An estimate of the transfer function of the channel is required by the MLSE equalizer in order to be able to compensate for the channel ISI effect.
The problem of estimating the channel is made worse by the dynamic nature of the channel. The equalizer must continuously adapt to varying channel characteristics as the mobile station moves through different multipath environments. For this reason, the training sequence is included in each burst.
The MLSE equalizer operates by scanning all possible data sequences that could have been transmitted, computing the corresponding receiver input sequences, comparing them with the actual input sequences received by computing metric parameters and selecting the sequence yielding the highest likelihood of being transmitted. Considering that ISI can be viewed as unintentional coding by the channel, the Viterbi algorithm used in the MLSE equalizer can be effective not only in decoding convolutional code sequences but in combating ISI. Typically, the MLSE equalizer comprises a matched filter (i.e. FIR filter) having N taps coupled to a Viterbi processor. The output of the equalizer is input to the Viterbi processor which finds the most likely data sequence transmitted.
A key constraint of the equalizer, whether the equalization technique is linear, DFE, MLSE or otherwise, is that it must have an accurate estimate of the channel in order to achieve good performance. GSM and other TDMA type communications systems provide for this by transmitting a burst comprising unknown data and a known training sequence. The purpose of the training sequence is to provide a basis for channel estimation. Several methods of channel estimation are known in the art and include, for example, a correlation method and a least squares method.
All the above methods, however, assume a known channel order which, in reality is unknown. Accurate knowledge of the channel order and channel tap coefficients yields optimum performance of the MLSE equalizer. The FIR filter used preferably is long enough to include the channel impulse response (CIR). The number of taps of the filter is important in that it relates to the number of path delays that must be equalized. Note that in the case of GSM, a number of taps N between 5 and 7 typically yields satisfactory results.
Note that the problem of determining the channel order is made worse for radio channels that are characterized by rapidly changing impulse response functions. The taps of the filters used to simulate these channels can be modeled as zero mean, complex, random processes. Thus, the CIR must be measured from burst to burst and cannot be assumed to be fixed.
One possible approach in choosing the number of taps (i.e. the channel order) is to assume a constant channel order. This approach is described in the book “GSM System Engineering,” A. Mehrotra, 1997, Chapter 6. This approach, however, has a disadvantage in that when the channel is shorter than the estimated channel length, the noise floor of the receiver increases due to the selection of non-relevant taps. The non-relevant taps result in added noise that is correlated for each symbol since the channel estimate is used in determining each received symbol. Note that although there is always some error in the channel estimate, it is exaggerated when the channel length is estimated to be longer than it actually is.
Another disadvantage is that when the channel is longer than the estimated channel length some channel taps will be omitted when they should not be. In this case, the MLSE equalizer will not be able to eliminate the intersymbol interference completely since the entire channel impulse response is not being modeled.
Other prior art channel estimation techniques involve averaging received training sequence symbols over time. A disadvantage of this technique is that training sequence history must be collected and stored in order to perform channel estimation. This increases the memory resources that are required, since past training sequence symbols must be stored to generate the history.